Hi, On Tue, Mar 10, 2015 at 9:27 AM, Sturla Molden <sturla.molden@gmail.com> wrote:
Alistair Miles <alimanfoo@googlemail.com> wrote:
I'm trying to calculate correlation coefficients and looking at the np.corrcoef function. It has bias and ddof arguments, however when I try different values of ddof with test data the results are always the same, i.e., changing ddof has no effect. From some back-of-the-envelope algebra I reckon the n/(n-ddof) normalisations should get cancelled out when calculating correlation coefficients from a covariance matrix, and therefore the ddof (and bias) arguments to np.corrcoef are redundant.
I'd be very grateful if someone could verify this is true or tell me if I've missed something.
You are right. It should cancel out or np.corrcoef would be wrong. The sample size does not go into the Pearson product-moment correlation.
Oh dear - that's embarrassing. https://en.wikipedia.org/wiki/Pearson_product-moment_correlation_coefficient I guess we should deprecate the 'bias' and 'ddof' input arguments asap. Cheers, Matthew